Armed Conflict, Household Victimization, and Child Health in Côte d''''Ivoire ppt

H i C N Households in Conflict Network The Institute of Development Studies - at the University of Sussex - Falmer - Brighton - BN1 9RE www.hicn.org Armed Conflict, Household Victimizat

H i C N Households in Conflict Network

The Institute of Development Studies - at the University of Sussex - Falmer - Brighton - BN1 9RE

www.hicn.org

Armed Conflict, Household Victimization,

HiCN Working Paper 115

August 2012

Abstract: We examine the effect of the 2002-2007 civil conflict in Côte d'Ivoire on children's

health status using household surveys collected before, during, and after the conflict, and

information on the exact location and date of conflict events Our identification strategy relies on exploiting both temporal and spatial variation across birth cohorts to measure children's exposure

to the conflict We find that children from regions more affected by the conflict suffered

significant health setbacks compared with children from less affected regions We further

examine possible war impact mechanisms using rich data on households' experience of war from the post-conflict survey Our results suggest that conflict-induced economic losses, health

impairment, displacement, and other forms of victimization are important channels through which conflict negatively impacts child health

Keywords: child health, conflict, height-for-age, sub-Saharan Africa

1

Olga Shemyakina would like to thank Georgia Institute of Technology for financial support We are grateful to the National Statistical Institute and the Ministry of Planning and Development in Côte d'Ivoire for their permission to

use the 2002 and 2008 HLSS (Enquêtes sur le Niveau de Vie (ENV)) for this project We are grateful to Adam

Pellillo, Emilia Simeonova, and conference participants at the 3rd Conference of the International Society for Child Indicators (York, July 2011), the 81st Southern Economic Association Annual Meeting (Washington, November 2011), and the 7th Households in Conflict Network Workshop (Barcelona, November 2011) for helpful comments and discussions The views expressed in this paper are those of the authors and do not necessarily reflect those of the IMF or IMF policy, or those of granting and funding agencies

2 International Monetary Fund IMF Institute

3 Georgia Institute of Technology School of Economics

I Introduction

Poor child health is a major problem facing policymakers in developing countries Access to adequate nutrition and health services is especially problematic in countries affected by armed conflict Recent studies show that armed conflict often triggers declines in household incomes, wealth, adult health, employment and security, and through these channels can have an adverse impact on child health (Stein et al 2004; Bundervoet et al., 2009; Kondylis, 2010; Akresh et al., 2011a; Akresh et al., 2011b; Mansour and Rees, 2011) In this paper, we further explore the link between armed conflict and child health by studying the impact of the 2002-2007 conflict in Côte d'Ivoire on children's height-for-age, a widely recognized measure of long-term health Using rich post-conflict survey data on war-related experiences, we also analyze several

mechanisms by which armed conflict can hamper child health Our identification strategy relies

on exploiting both temporal and spatial variation across birth cohorts to measure children's exposure to the conflict

Our results indicate that the height for age z-scores of children who were surveyed during and shortly after the conflict and who lived in conflict-affected regions was on average 0.414 standard deviations lower than that of children who were less exposed to the conflict The health

of young children was also negatively affected by the conflict-induced victimization reported by heads of households, for instance economic losses, health impairment, and displacement This effect is stronger in conflict-affected regions and for young boys The negative impact is also stronger for children exposed to the conflict for longer periods Our findings are robust to

including child controls, household controls, province-of-residence fixed effects, month-of-birth

fixed effects, and province-specific time effects that allow for differential trends in cohort health

The analysis is based on data from three cross-sectional household surveys collected before, during, and after the conflict These are the 2002 and 2008 Household Living Standards Surveys (HLSS) ENV-2002 and ENV-2008 and the 2006 Multiple Indicator Cluster Survey (MICS3) The surveys were undertaken by the National Institute of Statistics in Côte d'Ivoire in collaboration with UNICEF The ENV-2008 was designed as a post-conflict survey and provides

a unique set of variables on the experience of war, including questions about loss of productive economic assets, effects of the conflict on adult health, displacement, and on other forms of

victimization We use these data to obtain measures of household-level exposure to the war and examine the added impact of conflict-related victimization on child health To identify conflict-affected areas, we use data on the exact locations of battles, violence against civilians, riots, and transfer of power between parties from the recently released Armed Conflict Location and

Events Dataset (ACLED) (Raleigh et al., 2010)

Our analysis is most closely related to studies of the link between armed conflict and child health in sub-Saharan countries, which document a strong detrimental impact (see, e.g., Bundervoet et al., 2009; Akresh et al., 2011a, 2011b) We contribute to this literature in two

ways First, we employ both pre- and post-conflict data on child health, which enables us to

control for the baseline (pre-conflict) level of child health While post-conflict data and outcomes have been extensively studied in the literature, pre-war data are rarely available for conflict-affected regions Second, rich information on conflict-related victimization in the post-war survey allows us to analyze the joint impact on child health of being in the conflict zone during

the war and suffering from conflict-induced victimization during the same period Thus, we are

able to examine and quantify the impact of several mechanisms which may explain the negative health impact of armed conflict identified in the literature

Our study also contributes to a relatively scarce literature on economic development in West African economies (World Bank, 2012, pp 136) There are few recent studies on Côte d'Ivoire and these do not focus on health.4 One exception is the comparative study of Strauss (1990) who shows that in the mid-1980s Ivorian children in rural areas fared well relative to other African nations in terms of nutritional status In 1985 stunting rates in rural Côte d'Ivoire were half the African average, but twenty times larger than in the United States In a related study, Thomas et al (1996) examine the effects of the 1980s macroeconomic adjustment policies

in Côte d'Ivoire on child and adult health Focusing on height-for-age, weight-for-height, and body mass index and using the 1987-1988 HLSS, they find that the health of children (up to 12 years old) and adults alike was negatively impacted by macroeconomic adjustment, in particular due to an increase in relative food prices and reduced availability and quality of health

infrastructure Larger negative effects are documented for males, children and adults, a result that

is echoed in our study

4

See Duflo and Udry (2003) for a study of intrahousehold resource allocation in Côte d'Ivoire

Our paper relates to the large literature that examines the causal impact of negative

shocks experienced in early childhood on adult health, education, and labor market outcomes (for a review, see Almond and Currie, 2011) In developing countries, individual height is

positively correlated with education, employment, and wages (Strauss and Thomas, 1998) Similarly, malnourishment during childhood contributes to poor schooling performance and negative adult socio-economic outcomes (Glewwe et al., 2001; Alderman et al., 2006) Impaired fetal growth is associated with significantly higher chances of cardio-vascular disease in adults, which suggests that poor nutrition early in life is difficult to reverse and may permanently

damage health (Barker, 1998) The process whereby the fetus adjusts to short-term changes in

their environment (referred to as programming), may be beneficial in the short run but is

detrimental to long-term health (Godfrey and Barker, 2000).5Since we are able to identify a strong, negative and potentially causal impact of the Ivorian conflict on cohorts of young

children in conflict-affected regions, our findings suggest that policy interventions should aim to mitigate the unfavorable socio-economic outcomes that can be expected for these cohorts during adulthood

The remainder of the paper is organized as follows In Section II we describe the

historical context of the Ivorian conflict Section III presents the data, the estimation strategy, our baseline results, and robustness checks In Section IV we discuss and provide evidence of the channels through which armed conflict impacts child health In Section V we conclude.6

II Spatial and Temporal Intensity of the 2002-2007 Conflict in Côte d'Ivoire

Côte d'Ivoire, the world's leading exporter of cocoa, enjoyed a long period of political stability and economic development following its declaration of independence in 1960 With an average real GDP growth rate of 4.4 percent per year during 1965-1990, Côte d'Ivoire became an

economic powerhouse in West Africa and an attractive destination for foreign investment and migrant workers from neighboring countries.7 Political unrest followed the death of long-

standing President Felix Houphouet-Boigny in 1993, with a number of coups d'état taking place

5 While the literature generally indicates that the negative shocks to health in early childhood and in utero shocks are

irreversible, recent studies suggest there is potential for a reversal of this impact (Mani, forthcoming)

during the 1990s The decade ended with a military coup in December 1999 which caused a deep sociopolitical crisis Nevertheless, the power struggle that marked the 1990s was not uncommon

to a number of African countries transitioning to a multi-party elections-based system

The 2002-2007 civil conflict was rooted in controversies over nationality laws, voting rights and land reform As tensions flared over eligibility conditions for national elections,8 the armed conflict began in September 2002 with multiple attacks by rebel forces representing mostly the Muslim, northern parts of the country Violence erupted in several cities, including Abidjan in the south, Bouaké in the center, and Korhogo in the north (marked on a map of Côte

d'Ivoire in Figure 1) In the period that followed, the rebel forces (Forces Armées des Forces

Nouvelles) retreated to the northern and western parts of the country (UK Home Office, 2007),

where they established a "parallel administration, economy, treasury, judicial system, and

security structures" (UNSC, 2010, pp 7) The south remained under government control communal land disputes, fueled by a 1998 Rural Land Law that denied non-Ivorians the right to own land (Daudelin, 2003), were also common during the conflict.9

Inter-Delivery of basic social services in rebel-held areas remained limited throughout the conflict, and this limited delivery is an important channel through which the population was impacted by the conflict According to surveys analyzed in Fürst et al (2009), the three most important conflict-related problems reported by households in the western province of Man concerned health (48 percent), followed by the lack of food (29 percent) and the interruption of public services (13 percent) Precarious water distribution during the conflict compounded

existing health problems, with reports that only one fifth of water pumps in the rural north were operational (UNOCHA, 2004) Education services were also severely disrupted in the north, where 50 percent of school-age children were deprived of education by 2004 (Sany, 2010) It is also estimated that 70 percent of professional health workers and 80 percent of government-paid teachers abandoned their posts in the northern and western parts of the country (UNOCHA, 2004; Sany, 2010)

8 The 2000 constitution stipulated that presidential candidates be born in Côte d'Ivoire from Ivorian parents

9 The seeds of the conflict were sown in the mid-1990s when the concept of "Ivoirité" (or "Ivoiry-ness") entered the political discourse As the country has a mix of ethnically-diverse population, a large share of foreign workers, and naturalized first- and second generation Ivorians, the denial of voting rights, land rights, and hostility towards migrants led to tensions that culminated in the 2002-2007 conflict (Sany, 2010)

The initial, more violent phase of the war (2002-2004) was followed by a tense period marked by isolated bouts of violence (2005-2007) McGovern (2011) argues that this "no war, no peace" situation benefitted those who stood to gain from the conflict, thus supporting the idea of

an economic opportunity motive for armed conflict suggested by Collier and Hoeffler (2004) McGovern also points out that during the less violent phase of the conflict, armed checkpoints and roadblocks in Côte d'Ivoire were a widespread means for generating revenues for those manning them.10

Over the period many peace talks and negotiations took place with the aim of reunifying the country and restoring peace A timeline of events based on the reports of the UN Mission in Côte d'Ivoire (ONUCI) is shown in Figure 2 The conflict ended officially in March 2007 with the Ouagadougou Political Accord and agreements were soon reached to begin disarmament and reintegrate the rebels into the national armed forces While the conflict resulted in relatively few casualties (600 battle fatalities per year in the initial phase) compared to ten times as much for the average civil war in the Battle Deaths Dataset (UCDP/PRIO, 2009), it led to significant population movements The economic impact of the conflict during the period 2002-2007 was also substantial, with an average per capita GPD growth rate of −1.5 percent, the second lowest

in the region, and an increase in the national poverty rate by 10 percentage points to 48 percent

in 2008.11

To identify conflict-affected regions, we use information from the ACLED database containing the exact dates and locations of violent incidents during the conflict, including riots, protests, armed battles, and violence against civilians We match conflict events within each location and for each year to children's residence and year-of-birth in the surveys We define conflict regions as those provinces for which ACLED reports at least one conflict event from September 2002 to December 2007 Figure 3 depicts the spatial distribution of conflict events recorded in the ACLED dataset With the exception of Abidjan, the economic and former

political capital of Côte d'Ivoire, provinces with a higher incidence of violence, shown in darker shades, are concentrated in the rebel-held, northern and western parts of the country

In Figure 3 western Côte d'Ivoire stands out as the area most affected by high-intensity conflict (based on the frequency of conflict events reported in the ACLED dataset) Several reasons may explain this pattern First, fertile cocoa-growing regions of western Côte d'Ivoire had long-standing tensions between indigenous ethnic groups and non-Ivorians (mostly of

Burkinabe and Malian origin) over property and land rights (Mitchell, 2011) Second, the region hosts large numbers of Liberian refugees who in the aftermath of the 1999-2003 Liberian Civil War settled in a special refugee zone extending over four western provinces About one third of the population in these provinces is of foreign origin (Kuhlman, 2002, pp 18) and foreigners were targeted during the conflict.12 Third, during the second phase of the conflict the western regions witnessed a large number of attacks by local militarized groups, including attacks against United Nations bases and property (UNOCHA, 2006a, 2006b).13

III Data and Methods

III.1 Household Surveys

The analysis employs data from the nationally representative 2002 and 2008 Côte d'Ivoire HLSS,

as well as the 2006 MICS3 dataset, all of which provide anthropometric information for 15,443 children aged 6-60 months at the time of the survey Our health measure is height-for-age z-scores, which is a commonly used indicator of long-run child nutritional status and health

(Martorell and Habicht, 1986) We compute z-scores for each child's height-for-age using World Health Organization (WHO) reference datasets The z-score is the difference between surveyed children's height and the average height from the WHO reference population of same-age

children, divided by the standard deviation of the latter As shown in Table 1, average age z-scores for Ivorian children were lower than for the international reference population by almost two standard deviations in 2002, and 1.5 standard deviations in the 2006 and 2008

height-for-surveys

12 In particular, hostilities resurfaced in Côte d'Ivoire between the same ethnic groups which had fought on the Liberian side of the border during the 1999-2003 Liberian War Several UN documents report hostilities in the Liberian community during the Ivorian conflict (UNOCHA 2003a, 2003b) According to McGovern (2011, pp 207), both parties to the conflict often attributed especially violent events to Liberian militias He writes that the reason was that Ivorians "were keen to preserve the idea they had of themselves as being sophisticated." McGovern argues that this strategy of image preservation helped to contain the violence

13 Chelpi-den-Hamer (2011) provides a detailed account of the motivations and activities of armed factions in western Côte d'Ivoire during the conflict

Across surveys, average height-for-age z-scores are also higher in conflict regions

compared to non-conflict regions (Table 1), suggesting that child health improved during

2002-2007 despite the conflict Notably, there are no significant differences in average age across surveys, nor between conflict and non-conflict regions within each survey This reduces the likelihood that age differentials explain the war impact we seek to estimate Comparing the remaining variable means across conflict and non-conflict regions (Table 1, columns 4-5), we note statistically significant differences in the share of children of various ethnicities and

religions Similarly, mother's education is slightly higher in the regions more exposed to the conflict, and children in conflict regions are less likely to reside in rural areas but more likely to come from poorer households.14 We include these variables as controls in our regression analysis

to ensure that our results are not driven by these differences Since poverty can be either a condition for or an outcome of the conflict, we also perform regression analysis for the samples

pre-of poor and non-poor households.15

III.2 Baseline Specification

We begin by estimating the following difference-in-differences specification:

(1)

HAZijt  j t  jt  (Conflict Region *War Cohort )  ijt

where HAZijt is the height-for-age z-score for child i in province j born during year t; jare the province fixed effects, tare birth-cohort fixed effects, jt are province-specific trends in cohort health, and ijtis a random, idiosyncratic error term Indicator variables for female children and rural residence are included in all regressions The 'War Cohort' variable identifies children measured in the 2006 and 2008 surveys who were thus exposed to the conflict either in infancy

or in utero Note that while the 2008 survey includes only data for children born after the

conflict, the 2006 survey contains data for children born between August 2001 and April 2006, and thus covers children born before and during the conflict In this specification, the main

coefficient of interest is on the difference-in-differences term (1), which captures the average impact of conflict on the health of children in the war cohort

We consider several variations of the specification in Eq 1 to explore the impact of conflict on child health by exploiting variation in the duration of exposure to the conflict For instance we replace the 'War Cohort' variable with a continuous measure of the duration of exposure to the conflict (in months) and then with indicator variables for zero months of

exposure (reference category), exposure between one and 24 months, and exposure of at least 25 months To allow for gender differentials in the health impact of the conflict, we also estimate

Eq 1 with additional interaction terms with a female dummy In additional specifications we assess the sensitivity of our main results to adding controls for child, household head, and

mother‟s characteristics

III Empirical Results

III.1 Baseline Regressions

Table 2 presents baseline results from OLS regressions of height-for-age z-scores on conflict incidence in children's province-of-residence for the full sample of children from the three

surveys The results indicate that children with in utero or early childhood exposure to the

conflict and who lived in conflict-affected regions had height-for-age z-scores 0.414 standard deviations (s.d.) lower than those born during the same period who lived in less affected regions (column 1), or 0.428 s.d when allowing for a gender-specific impact We then exploit individual variation in the duration of exposure to the war by replacing the 'War Cohort' dummy with

indicator variables for exposure to the conflict either shorter or longer than 24 months (columns 3-4) This replacement yields estimates of the impact that are higher for younger children and lower for older children An additional month of exposure to the war reduces the height-for-age z-score by 0.012 s.d on average (columns 5-6)

The coefficient estimates on the triple interaction term with the female dummy do not reveal a gender differential in any of the specifications considered The finding is not surprising

in light of other anthropometric studies on sub-Saharan Africa Unlike the research on child health and famines (Mu and Zhang, 2008) or natural disasters (Rose, 1999) focusing on Asian countries, there is no consistent evidence of sex bias in early child health studies on sub-Saharan

African countries, either during tranquil times or after negative shocks.16 For example, Alderman

et al (2006) study anthropometric outcomes in Zimbabwe and do not find significant differences

by gender in a sample of young children Budervoet et al (2009), Akresh et al (2011a, 2011b) show that health outcomes for girls and boys were equally impacted by the Burundian, Rwandan, and Eritrean-Ethiopian conflicts respectively Using data from the 1986 HLSS, Strauss (1990) documents marginally lower, yet statistically insignificant height-for-age and weight-for-height for boys living in the rural areas of Côte d'Ivoire

Table 3 contains the baseline specifications that are augmented with several sets of

control variables In particular, we control for child ethnicity and religion, characteristics of the household head (age, marital status, education) and characteristics of the child's mother (age and education) This ensures that the factors we found to differ significantly between exposed and non-exposed households (Table 1) do not bias our results F-tests for the joint significance of these control variables show that the only factor that does not systematically affect children's health is their ethnic background In these regressions the average health impact of conflict is of similar magnitude to that in the specifications without controls

III.2 Robustness Checks

III.2.1 Alternative Baseline Cohort

It is possible that pre-conflict events affected the health of our baseline cohort thus confounding our baseline results A major event that may have affected the health of children in the pre-war cohort (and surveyed in 2002 and 2006) is a military coup that led to a change in government in Côte d'Ivoire on December 26, 1999 The coup had a significant impact on the Ivorian economy, leading to contraction of real GDP growth of -0.2 percent during 1999-2000 compared to an average growth of 5.8 percent during 1994-1998 (Doré et al., 2003) Following the coup, public investment projects were postponed, private investment collapsed, social spending was cut back, and migrant workers fled following ethnic clashes in the south From 1998 to 2002, the national poverty rate rose by five percentage points to 38.4 percent It is thus plausible that children born after December 1999 experienced a decline in their well-being as the crisis unfolded Thus, children born between January 2000 and August 2002 in the pre-war survey may constitute a

16 One recent exception is Akresh et al (2011a) who find that crop failure in rural Burundi has a stronger negative health impact on young girls

poor baseline group to study the impact of the 2002-2007 civil conflict.17 Furthermore, children born during the same period and surveyed in 2006 could also be a poor treatment group as they were exposed to two major shocks during their lifetimes, both the coup and the conflict As a robustness check, we perform our regressions on the sample that excludes children born between January 2000 and August 2002 (from the 2002 and 2006 surveys), the month before the civil conflict erupted Therefore, our new control group includes only children born before the coup and children born after the conflict started who lived in less affected regions

The results (Table 4) show that children born during the 2002-2007 conflict had

significantly worse health compared to those born before the December 1999 coup In these specifications we control for child ethnicity and religion, as well as characteristics of the

household head and child‟s mother Notably, the coefficient estimates on the interaction term (Conflict Region*War Cohort) are at least twice as large when we exclude the "post-coup" children compared to the baseline results (Tables 2-3) Our earlier results could thus be

interpreted as conservative estimates of the impact of the Ivorian conflict on children's health III.2.2 Results Across Sub-samples

We further evaluate the impact of conflict on children from different types of households and by gender In Table 5 we present the estimates of regressions on sub-samples of children from poor and non-poor households, girls vs boys, rural vs urban areas, and for children from households headed by individuals with some education and without any education Columns 1-2 report results of the baseline regression models (not including any controls) for the poor and non-poor sub-samples Poor households are identified using an assets-based index which refers to the quality of the dwelling, and access to the grid and utilities.18 We find that war-exposed children

in conflict-affected areas were negatively impacted in both poor and non-poor households, losing

on average 0.303-0.549 standard deviations in their height-for-age z-scores (columns 1-2).19

When we separate the sample into boys and girls (Table 5, columns 3-4), we find that both girls and boys in the war-exposed cohort who lived in the conflict-affected regions suffered important health setbacks (significant at the 5 and 10 percent level, respectively) The results are consistent with findings of no sex bias from case studies of the Rwandan and Eritrea-Ethiopian conflicts (Akresh et al., 2011a, 2011b) The results for the sub-samples of children living in rural/urban households and respectively in households headed by educated/uneducated

individuals reveal that the war cohort of children who also lived in conflict-affected areas was impacted more in rural households and in households headed by individuals without education The latter result suggests that heads of households with higher educational status were better able

to protect their children from the effects of the crisis

IV Exploring the War Impact Mechanisms

IV.1 Measures of Conflict-Induced Victimization

In this section we go one step further in analyzing the impact of conflict on child health by

focusing on alternative measures of child exposure to the armed conflict Specifically, we

examine several types of conflict-related victimization as channels through which the war can adversely impact child development We compute four household-level indices of conflict-

induced victimization based on war experiences reported by the heads of households in the 2008 survey The indices are calculated as simple sums of underlying questions, and capture a wide range of types of distress, including loss of productive economic assets and income, health impairment, displacement, and other forms of victimization.20

The 'economic losses' index is based on seven questions about economic damage during the war, such as whether a household experienced a decline in the assets or revenues, lost their farm or livestock, employment, or other productive assets The 'health impairment' index is based

on questions such as whether the household head experienced war-related nightmares, stress, fear

or anxiety, illness, or had to see a psychologist The 'displacement' index refers to whether the household head went into hiding because of the conflict, had to move his household, or was displaced at the time of the conflict or interview (mid-2008).21 Finally, the 'victim of violence' index captures additional ways in which the household head may have been victimized during the conflict, for instance by being robbed, subjected to sexual violence (including rape), or

wounded; or by experiencing war-related deaths in the household Table 6 lists the questions underlying each index T-tests for the differences in mean values of the components show that negative economic shocks and displacement were more prevalent in conflict-affected regions, while households experienced relatively similar levels of health distress inside and outside

conflict regions

We spatially examine the experience of war in Figure 4, which depicts conflict

victimization maps according to the province-level share of households that report at least one level of victimization Darker shades represent provinces with a higher greater of households giving a 'yes' response to at least one question in each index Panels A and B suggest that

economic losses, and to some extent health effects, were more prevalent in the northern areas controlled by rebel forces The displacement and victim of war violence indices (Panels C and D) appear to visually overlap the best with the ACLED-based conflict map (Figure 3), with more frequent reports of victimization in the western parts of the country, especially along the border with Liberia The share of households reporting at least one level of victimization along the four dimensions considered, correlates positively with conflict intensity proxied by the number of conflict events in the ACLED dataset (Appendix Table A2) and the correlation coefficients range between 0.097 (economic losses) and 0.198 (victim of violence) The province-level

victimization measures are also strongly correlated with one another, with the highest

correlations (statistically significant at the 1 percent level) being between economic losses and health impairment (0.768); and displacement and victim of violence (0.859)

IV.2 Selection into Victimization

One concern in using self-reported victimization data to explore the war impact mechanisms is that households that report victimization may belong to a select sample that was targeted for

21 We focus on the impact of displacement on migrating households rather than receiving communities For an analysis of the health impact of hosting refugees, see Baez (2011)

violence due to their observable or unobservable characteristics To determine the extent to which victimization status is correlated with observables, we estimate regressions of each

victimization index on a rich set of (mostly time-invariant) characteristics of the heads of

households, including their ethnicity and religion, rural residence, age, marital status, education, and gender The results are reported for the full sample and for non-migrant households in Table

7 We find little evidence of a systematic pattern of selection into victimization across all types

of victimization based on observable characteristics Older heads of households report more conflict-induced economic losses and health effects (columns 1-4), whereas more educated ones have higher levels of self-reported displacement and victimhood (columns 5-8)

For ethnic groups the results are more mixed, with the Southern Mandé (who live

primarily in the western regions extensively affected by the conflict) systematically reporting more conflict-related health impairment, displacement or other victimhood (than the Akan ethnic group, the reference category) This result is consistent with the visual examination of the

victimization maps (Figure 4), the conflict region map (Figure 3), as well as reports on the

intensity of conflict events Naturalized Ivorians, who constitute only 0.3 percent of the dataset, are significantly less likely to report conflict-related victimization We would have expected the opposite effect as foreigners were targeted during the conflict but many ethnic groups native to Côte d'Ivoire are also found in neighboring countries, and thus the ethnic status may not be a good basis for classifying individuals as outsiders (Levinson, 1998) McGovern (2011, pp 71) also points out that in western Côte d'Ivoire, "anyone not born in a village is technically a

„stranger‟…" and that men moving 20 or 2,000 kilometers away from their native villages would

be treated as foreigners in their new place of residence

F-tests of the joint significance of household head's characteristics in explaining the reported level of victimization (Table 7) indicate that religion rarely plays a role, while ethnicity and other characteristics are sometimes important.22 Overall, the regression results indicate that there is no strong pattern regarding the characteristics of households that report different forms

self-of victimization Nevertheless, we allow for systematic patterns in our regressions exploring the war impact mechanisms by including indicators for child ethnicity, which is strongly correlated with household head ethnicity, and the main results remain robust and statistically significant

22 In results not reported, we find that economic status in 2008, proxied by an indicator variable for non-poor households, is positively correlated with reports of economic losses However, since income status is likely to change during the conflict, we do not include the poverty indicator in the regressions reported.

We noted that conflict-affected regions experienced high levels of migration, which may have been caused by exposure to the conflict and potential or actual victimization Selective migration out of conflict areas would lead to a downward bias on the war impact coefficient if the households that stayed behind were consistently worse off in terms of child health Put

differently, the war impact coefficients may also be picking up negative selection into staying in conflict-affected regions in addition to the conflict effect To examine this issue, we perform two tests The first is to compare the characteristics of households in conflict vs non-conflict regions before and after the conflict The results are reported in Table A3 and suggest that there are no systematic changes in the average household profile before and after the war neither in nor outside conflict regions The only exception is a smaller post-conflict share of foreigners

everywhere which likely reflects conflict-induced migration to neighboring countries

The second test is to look for evidence of differential exposure to victimization among migrant and non-migrant households We do so using alternative definitions of conflict-induced

migration The first definition refers to households that moved during the conflict, for any

reason, and is based on the reported duration of their residency in the current location This is the migration variable used so far in the analysis Migrant households according to this definition represent 15 percent of the post-conflict sample The second definition refers to households representing about 8 percent of the sample that responded affirmatively to the question "Have you been displaced by the conflict?" The third definition identifies households that responded affirmatively to the question "Did your household move because of the conflict?" and they account for about 10 percent of the sample

To test for the differential exposure to victimization across migrant and non-migrant households, we regress each component of our victimization indices on the three migrant

variables defined above, while controlling for economic status (poor vs non-poor), area of residence (rural vs urban), household head characteristics, and province fixed effects The

results, reported in Table A4 (columns 1-2), indicate that households that migrated during the conflict, regardless of the reason for doing so, were significantly more likely to have their assets and properties damaged, lose employment, experience war-related ailments or sickness, seek medical help, and experience conflict-related deaths in the household Many of these households were displaced by the conflict In addition, households in which the head of households reported being displaced by the conflict (columns 3-4) or having to move the household because of the

conflict (columns 5-6) were statistically significantly more likely to report all types of

victimization

Overall, these results suggest that non-migrant households tend to report less

victimization, whereas migrant households, especially those displaced by the conflict, tend to

report more victimization Put differently, there is evidence of positive selection in conflict

regions and negative selection into migration It follows that any estimated impact of induced victimization on child health in conflict regions is likely to underestimate the true impact

conflict-of conflict due to the positive selection into staying in conflict affected regions

IV.3 Identifying the Mechanisms

To examine the potential role played by each of the four forms of victimization discussed, we estimate two sets of specifications First, we examine the cross-sectional impact of conflict-induced victimization using the post-war (2008) survey We estimate the following specification: (2)

HAZijt  j  t  jt  (Victimization )  ijt

The coefficient of interest, 3, is an estimate of the direct effect of victimization on the health of children in the war cohort.23 The results are reported in Table 8 for each victimization index, for the full sample and by gender Since non-migrant households are less likely to be victimized by the war, we show the estimates separately for all households (first two rows) and non-migrant households (next two rows) Household-level victimization had a negative impact

on children's height-for-age z-scores, with signs mostly negative for either sample, but the

impact estimates are statistically significant only for the 'economic losses' index An additional 'yes' response to the questions about loss of productive assets, employment or income, is

associated with a height-for-age z-score for children in the war cohort lower by between 0.098 s.d and 0.142 s.d depending on the set of controls (column 1) The result is more pronounced for boys (column 3), but there are no systematic gender differences for any other form of

victimization (columns 4-12).A test for the equality of coefficient estimates across migrant and non-migrant households, with p-values reported in the last two rows of the table, indicates that the effects are statistically equal regardless of migration status.24

Second, we assess whether the war impact on child health identified in our baseline results varies with the extent of victimization experienced by households during the conflict To

do so, we exploit the additional cross-sectional variation given by children living in households victimized by the war, and interact the difference-in-differences term (Conflict Region*War

Cohort) from Eq 1 with each of the four victimization indices Since victimization variables are available only in the post-conflict survey,25 this procedure amounts to estimating:

(3)

HAZijt  j t jt  (Conflict Region *Victimization )   (Victimization )+ ijt

By estimating Eq 3 we look for a differential impact of conflict on child health according to the degree of conflict-related victimization experienced by the heads of households This effect is captured by the estimate for4 The specification allows us to assess the joint impact of living in

a conflict-affected region and in a victimized household (compared to all other households), and thus to examine the role of different channels through which conflict may affect child health As

in previous specifications, we control for average health differences across genders and area of residence (rural/urban), and add an interaction term with the female dummy to explore gender differentials in the estimated magnitude for4.26

Table 9 presents the results for the full sample both with and without controls for child, household head, and mother's characteristics The results suggest that the negative impact of the conflict on children's height-for-age z-scores is larger for children living in households

victimized by the war The estimated marginal impacts range from −0.291 (column 2) to −0.783 s.d (column 12) for each additional positive answer to the underlying questions, depending on the index and the set of controls For non-migrant households, the results are qualitatively similar

to the full sample for all forms of victimization other than displacement (Table 10) For the latter, the estimated coefficients on the interaction term for non-migrant households are similar in magnitude to those based on the full sample but less precisely estimated (columns 7-9) This result is consistent with the fact that non-migrants are less likely to report conflict-related

displacement.27

Overall, there is no strong evidence that the impact of the conflict on the health of

children living in households victimized by the war depends on migration status In

25 This implies that (Conflict Region*War Cohort*Victimization) is equal to (Conflict Region*Victimization) and (War Cohort*Victimization) is the same as (Victimization)

26

The estimated coefficients on the interaction terms with the female dummy, namely (Female*Conflict),

(Female*War cohort) and (Female*Victimization), are not shown in the tables for simplicity, but are included all specifications We consistently find that these variables have statistically insignificant joint effect on height-for-age 27

It also strengthens our confidence in the quality of self-reported victimization variables

affected areas, possible war impact mechanisms such as economic losses, health impairment, and

being a victim of violence, are factors that negatively affect the health of children from all

households, while conflict-induced displacement has a stronger impact in migrant households

V Discussion and Conclusions

We examined the effect of the 2002-2007 armed conflict in Côte d'Ivoire on children's for-age z-scores using data from three household surveys respectively collected before, during and after the conflict, coupled with information on the location of conflict events In the results

height-we have presented, children aged 6-60 months that lived in conflict-affected areas suffered significant health setbacks compared to those in less affected areas The negative impact is stronger for children exposed to the conflict for longer periods In line with other studies of child health in sub-Saharan African countries, we did not find any evidence of sex bias

Studies on the consequences of armed conflict have proposed several mechanisms

through which war affects populations, including destruction of economic assets and loss of income, lower access to public infrastructure, and significant population movements We were also able to document the importance of different war impact mechanisms using a rich set of variables on the experience of war by heads of households available in the post-conflict survey Our results suggest that conflict-related economic losses, health impairment, displacement, and other forms of victimization have a large and negative effect on child health in conflict regions.28These results thus help explain the adverse effects of armed conflict identified in the literature

Several recent case studies of the Ivorian conflict document the state of the health

infrastructure and households' coping strategies during the conflict, providing support for our findings as well as complementary views.29 For example, Fürst et al (2010) assess the

socioeconomic status of households using pre- and post-war panel data on households from the conflict-affected western region of Man Almost three quarters of the sampled households

attributed economic difficulties to the conflict Households also reported turning to agricultural production to make ends meet, which suggests a pattern of changed livelihoods Nevertheless, there are no differences in resilience or coping strategies by income, nor statistically significant

28

Our results should be interpreted with some care as they may be subject to survivor bias

29 These analyses use pre- and post-conflict data on relatively small samples of households from conflict-affected regions and combine statistical analysis with anecdotal evidence to examine the economic consequences of conflict

on the affected population

socioeconomic dynamics By contrast, we found that households with educated household heads were better able to prevent the negative impact of war on children's health At the same time, children in poor and non-poor households were equally impacted One reason explaining the different conclusions may be the relatively limited geographical focus of the surveys used by Fürst et al (2010) Another may be that our difference-in-differences approach allows us to compare changes in child health in conflict regions relative to less affected areas using both before and after data, thus controlling for trends elsewhere in the country

In a companion study based on the same dataset, Fürst et al (2009) report significant deterioration in access to health services and pharmacies in the aftermath of the conflict In 2003 interviewed households in the western region of Man mention a higher incidence of tropical diseases, including malaria The majority of deaths in this community are attributed to diseases rather than the conflict itself Betsi et al (2006) utilize survey data representative of towns in the central, northern, and western regions of Côte d'Ivoire, collected around the same time, and document a large reduction in the number of health facilities and health personnel (especially doctors) In the two years following the start of the conflict, rebel-held regions lost between 75-

90 percent of health personnel, and 72-90 percent of health facilities were closed after looting or destruction Considering the relatively poor pre-conflict stock of health infrastructure, conflict-induced losses of health workers and facilities likely had a major impact on the health of

children, both directly and indirectly through the adults in the household In addition, the losses

of public health infrastructure at a time when it was most needed may have compounded existing health deficiencies To test this idea, data on pre- and post-conflict stock and quality of health infrastructure at the province or community level would be needed

Our findings also suggest that displacement is an important channel through which the war affects child health Displacement leads to reduced access to household resources and social networks The conflict ignited widespread harassment of foreigners in Côte d'Ivoire, including migrant workers from the region and refugees from Liberia and Sierra Leone living on the

outskirts of cities Estimates of migration vary greatly, reflecting difficulties in estimating the size of displaced populations Some reports indicate that by late-2002 the number of war-affected people had reached between 2.7 million (including the internally displaced) and four million (including evacuees and refugees to Burkina Faso, Guinea, Liberia, Mali, and Sierra Leone) (UNOCHA, 2003) Other sources indicate that in the first ten months more than 500,000 people

were displaced (UNICEF, 2003), of which more than two thirds were Burkinabe nationals

(Sakurai and Savadogo, 2009).30 The estimated magnitudes for most of our key parameters for the non-migrant sample are similar to those for the full sample The only notable difference is in the results for displacement, which suggest a stronger negative impact for children from migrant households.31

Two years after end of the conflict, Côte d'Ivoire faced once again an internal crisis occasionally marked by episodes of violence Nevertheless, the economy is now on the rebound and is receiving fresh inflows of foreign investment and development aid (IMF, 2011) By

statistically documenting the contribution of different war impact mechanisms to lowering child health in conflict regions, we can suggest policies to mitigate the adverse effects of the 2002-

2007 armed conflict on child health Interventions that target the conflict-affected areas and aim

at rehabilitating basic social services, restoring economic well-being (for instance, through cash transfers or employment programs), and assisting the return of the displaced would seem most fit

in trying to reverse the effects of the conflict Nevertheless, there is little research on which policy interventions can best mitigate the negative effects of war on well-being in general, and child health in particular As knowledge on the impact of conflict on child anthropometric

outcomes accumulates, more research into households' coping strategies and best public policy responses is needed

30 Martone (2003) offers comparable estimates―750,000 internally displaced people and 500,000 refugees Betsi et

al (2006) estimate that the conflict in the central, northern and western regions led to displacement of 40, 25 and 55 percent of the local population respectively and that about 1.8 million people had left rebel-held regions by mid-

2004

31 In our case, the ethnicities that are most likely to report conflict-induced displacement are the Akan (living in the south, including the Abidjan area) and the Southern Mandé (living in western Côte d'Ivoire)

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